arXiv:2606.18166v1 Announce Type: cross Abstract: Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human effort. Pre-Large Language Model (LLM) automation sped up this process, but could not resolve the complex language and multi-step attack patterns found in unstructured CTI reports. LLMs addressed previous limitations by using contextual reasoning to understand unstructured text. However, current evaluations rely on simplified, single-technique sentenc
The proliferation of open-source LLMs provides new opportunities to automate complex cybersecurity analysis, moving beyond previous limitations in understanding unstructured threat intelligence.
Improved classification of cyber threats using LLMs can significantly enhance proactive defense capabilities for organizations and nations, reducing human effort and improving response times.
The ability to accurately classify multi-label ATT&CK techniques with open-source LLMs in CTI reports will lead to more efficient and sophisticated cyber threat intelligence operations.
- · Cybersecurity firms
- · National defense agencies
- · Open-source AI foundations
- · Managed Security Service Providers
- · Legacy threat intelligence systems
- · Organizations with limited AI adoption
- · Manual CTI analysts
More accurate and faster identification of cyber attack patterns and techniques through automated LLM analysis.
A shift in cybersecurity spending towards AI-powered threat detection and intelligence platforms.
Enhanced resilience against sophisticated state-sponsored cyberattacks or large-scale criminal campaigns due to improved intelligence processing.
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Read at arXiv cs.LG